Image registration refers to the geometric alignment of a set of images. The set may consist of two or more digital images taken of a single scene at different times, from different sensors, or from different viewpoints. The goal of registration is to establish geometric correspondence between the images so that they may be transformed, compared, and analyzed in a common reference frame. This is of practical importance in many fields, including remote sensing, medical imaging, and computer vision.
In remote sensing image registration of satellite images from orbital sensors, intrinsic problems occur because of both geometric error and radiometric distortion misalignment of two or more images. Geometric errors can be caused by position, size and orientation of a pixel being altered during the acquisition process. The causes of such geometric errors include earth rotation during the acquisition process, land curvature, platform speed and altitude variations, and changes in topographical elevation and sensor parameters, among others. Radiometric distortions affect pixel-encoded radiance and are mainly caused by atmospheric conditions, scene illumination, sensor gains and satellite observation angles at the moment of the image acquisition.
Normally, it is necessary to first geometrically correct an image, and then correct its radiometry. However, without having prior knowledge of sensor geometric models and associated radiometric parameters, mutual information has not been widely used for remote sensing applications for several reasons.
For alignment of remote sensing images using manual methods, control points common to all images need to be carefully selected for geometric registration via a polynomial transformation. These processes are time-consuming and commonly introduce modeling errors, since the most reliable points may not be uniformly distributed.
Automated registration methods, because of the radiometric distortions of remotely sensed images, require that a transformation criterion (to maximize the mutual information when the images are geometrically aligned) has been previously determined using well known global optimization methods such as simulated annealing, genetic algorithm, and exhaustive search. Also, some algorithms break down when the source image is significantly different from the reference image in intensity and contrast variations (e.g., brightness reversals such as light/dark roads, reverse video, etc.), cross-sensor phenomenology, and non-linear differences (e.g., oblique viewing angles, earth curvature, dynamic scale variation, etc.). Therefore, the associated results lack efficiency, are not reliable, are computationally expensive, and are not suitable for real time applications, such as on-board unmanned aerial vehicle (UAV) mapping.
Absolute radiometric restoration of image time series from optical orbital sensors is a difficult task. This is because it is necessary to know all the conditions which influence radiometric distortion between the subject images, such as the sun's inclination angle, atmospheric conditions, sensor view angle and sensor gain. Such information may be not available for all acquired images by the same user, or for images acquired by different institutions, yet these data values are necessary to evaluate landscape changes in a multi-temporal series. While different from absolute radiometric restoration, the radiometric correction will calibrate all subject images to the radiometric conditions of the same reference image, but will not necessarily correct distortions from turbulence blur, aerosol blur, or path radiance.
Image registration based on the usual mutual information measure formulation contains a local maximum solution. Therefore, existing global optimization search algorithms seeking an optimal solution always get struck at the local maxima instead of reaching the global optimum solution, thus causing image misalignments.
There is a need for an image registration technique that overcomes the limitations of the prior art.